# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import List, Tuple, Type

import paddle

from .common import LayerNorm2d


class MaskDecoder(paddle.nn.Layer):
    def __init__(
        self,
        *,
        transformer_dim: int,
        transformer: paddle.nn.Layer,
        num_multimask_outputs: int = 3,
        activation: Type[paddle.nn.Layer] = paddle.nn.GELU,
        iou_head_depth: int = 3,
        iou_head_hidden_dim: int = 256
    ) -> None:
        """
        Predicts masks given an image and prompt embeddings, using a
        tranformer architecture.

        Arguments:
          transformer_dim (int): the channel dimension of the transformer
          transformer (nn.Module): the transformer used to predict masks
          num_multimask_outputs (int): the number of masks to predict
            when disambiguating masks
          activation (nn.Module): the type of activation to use when
            upscaling masks
          iou_head_depth (int): the depth of the MLP used to predict
            mask quality
          iou_head_hidden_dim (int): the hidden dimension of the MLP
            used to predict mask quality
        """
        super().__init__()
        self.transformer_dim = transformer_dim
        self.transformer = transformer
        self.num_multimask_outputs = num_multimask_outputs
        self.iou_token = paddle.nn.Embedding(1, transformer_dim)
        self.num_mask_tokens = num_multimask_outputs + 1
        self.mask_tokens = paddle.nn.Embedding(self.num_mask_tokens, transformer_dim)
        self.output_upscaling = paddle.nn.Sequential(
            paddle.nn.Conv2DTranspose(
                in_channels=transformer_dim,
                out_channels=transformer_dim // 4,
                kernel_size=2,
                stride=2,
            ),
            LayerNorm2d(transformer_dim // 4),
            activation(),
            paddle.nn.Conv2DTranspose(
                in_channels=transformer_dim // 4,
                out_channels=transformer_dim // 8,
                kernel_size=2,
                stride=2,
            ),
            activation(),
        )
        self.output_hypernetworks_mlps = paddle.nn.LayerList(
            sublayers=[
                MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) for i in range(self.num_mask_tokens)
            ]
        )
        self.iou_prediction_head = MLP(transformer_dim, iou_head_hidden_dim, self.num_mask_tokens, iou_head_depth)

    def forward(
        self,
        image_embeddings: paddle.Tensor,
        image_pe: paddle.Tensor,
        sparse_prompt_embeddings: paddle.Tensor,
        dense_prompt_embeddings: paddle.Tensor,
        multimask_output: bool,
    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
        """
        Predict masks given image and prompt embeddings.

        Arguments:
          image_embeddings (torch.Tensor): the embeddings from the image encoder
          image_pe (torch.Tensor): positional encoding with the shape of image_embeddings
          sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes
          dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs
          multimask_output (bool): Whether to return multiple masks or a single
            mask.

        Returns:
          torch.Tensor: batched predicted masks
          torch.Tensor: batched predictions of mask quality
        """
        masks, iou_pred = self.predict_masks(
            image_embeddings=image_embeddings,
            image_pe=image_pe,
            sparse_prompt_embeddings=sparse_prompt_embeddings,
            dense_prompt_embeddings=dense_prompt_embeddings,
        )

        if multimask_output:
            mask_slice = slice(1, None)
        else:
            mask_slice = slice(0, 1)
        masks = masks[:, mask_slice, :, :]
        iou_pred = iou_pred[:, mask_slice]

        return masks, iou_pred

    def predict_masks(
        self,
        image_embeddings: paddle.Tensor,
        image_pe: paddle.Tensor,
        sparse_prompt_embeddings: paddle.Tensor,
        dense_prompt_embeddings: paddle.Tensor,
    ) -> Tuple[paddle.Tensor, paddle.Tensor]:
        """Predicts masks. See 'forward' for more details."""
        output_tokens = paddle.concat(x=[self.iou_token.weight, self.mask_tokens.weight], axis=0)
        output_tokens = output_tokens.unsqueeze(axis=0).expand(shape=[sparse_prompt_embeddings.shape[0], -1, -1])
        tokens = paddle.concat(x=(output_tokens, sparse_prompt_embeddings), axis=1)
        src = paddle.repeat_interleave(image_embeddings, tokens.shape[0], axis=0)
        src = src + dense_prompt_embeddings
        pos_src = paddle.repeat_interleave(image_pe, tokens.shape[0], axis=0)
        b, c, h, w = src.shape
        hs, src = self.transformer(src, pos_src, tokens)

        iou_token_out = hs[:, (0), :]
        mask_tokens_out = hs[:, 1 : 1 + self.num_mask_tokens, :]
        x = src
        perm_0 = list(range(x.ndim))
        perm_0[1] = 2
        perm_0[2] = 1

        src = x.transpose(perm=perm_0).reshape([b, c, h, w])
        upscaled_embedding = self.output_upscaling(src)
        hyper_in_list: List[paddle.Tensor] = []
        for i in range(self.num_mask_tokens):
            hyper_in_list.append(self.output_hypernetworks_mlps[i](mask_tokens_out[:, (i), :]))
        hyper_in = paddle.stack(x=hyper_in_list, axis=1)
        b, c, h, w = upscaled_embedding.shape

        masks = (hyper_in @ upscaled_embedding.reshape([b, c, h * w])).reshape([b, -1, h, w])
        iou_pred = self.iou_prediction_head(iou_token_out)
        return masks, iou_pred


class MLP(paddle.nn.Layer):
    def __init__(
        self,
        input_dim: int,
        hidden_dim: int,
        output_dim: int,
        num_layers: int,
        sigmoid_output: bool = False,
    ) -> None:
        super().__init__()
        self.num_layers = num_layers
        h = [hidden_dim] * (num_layers - 1)
        self.layers = paddle.nn.LayerList(
            sublayers=(
                paddle.nn.Linear(in_features=n, out_features=k) for n, k in zip([input_dim] + h, h + [output_dim])
            )
        )
        self.sigmoid_output = sigmoid_output

    def forward(self, x):
        for i, layer in enumerate(self.layers):
            x = paddle.nn.functional.relu(x=layer(x)) if i < self.num_layers - 1 else layer(x)
        if self.sigmoid_output:
            x = paddle.nn.functional.sigmoid(x=x)
        return x
